Adaptively multi-modal contrastive fusion network for molecular properties prediction

Molecular property prediction has become the mainstream approach for revealing the underlying mechanisms of biomedical systems with molecular representations. Existing prediction methods based on deep learning typically learn features from molecules at a specific modality or simple fusion solution, failing to consider the inconsistency, complexity, and relationships inherent in multi-modal data. To solve this issue, an adaptively multi-modal contrastive fusion network (AMCFNet) is proposed to adaptively extract the complementary features from interaction and consensus between multi-modal representations for molecular property prediction of breast cancer. The proposed model begins with a two-stream feature extractor module, which learns both one-dimensional (1D) and two-dimensional (2D) molecular representations simultaneously. The basic part of the network is the adaptively contrastive fusion module, contrastively learning features between similar and different molecules with consensus scores, which can adaptively allocate weight to fuse semantic and structural information while avoiding cognitive gaps caused by inconsistencies within multi-modal. Additionally, the final complementary molecular representation is derived by integrating 1D, 2D, and fused 1D-2D features to enhance the prediction of molecular properties in breast cancer. The proposed AMCFNet model is evaluated on five estrogen receptor alpha (ER$\alpha$) and five compound public datasets, consistently outperforming state-of-the-art baselines in classification and regression tasks of molecular property prediction including single- and multi-modal methodologies.